In the vast majority of cases, an infection with the hepatitis C virus (HCV) leads to chronic hepatitis C. Chronic hepatitis C can develop into cirrhosis of the liver with portal hypertension complications, and can also develop into hepatocellular carcinoma.
One of the aims of treatment against an infection by HCV, more particularly against chronic hepatitis C, is to arrive at the stage where the attacks on the liver tissue induced by the viral infection regress or are even eliminated, or at least that they do not progress. In particular, this means that the risk which arises of complications and hepatocellular carcinoma can be reduced or eliminated.
Currently available treatments for achieving this aim are treatments which are aimed at eradicating the virus. In the first place, these treatments have to induce a significant reduction in the viral HCV load, so as to be able to obtain an undetectable level at the end of treatment.
Current anti-HCV treatments comprise the administration of a combination of pegylated interferon and ribavirin. These treatments are of long duration: they are generally administered over a period of at least 24 weeks and may last up to 48 weeks or even longer.
However, anti-HCV treatments cause major side effects for the patient.
Regarding interferon, the side effects are frequent and numerous. The most frequent side effect is that of influenza-like syndrome (fever, arthralgia, headaches, chills). Other possible side effects are: asthenia, weight loss, moderate hair loss, sleep problems, mood problems and irritability, which may have repercussions on daily life, difficulties with concentrating and skin dryness. Certain rare side effects, such as psychiatric problems, may be serious and have to be anticipated. Depression may occur in approximately 10% of cases. This has to be identified and treated, as it can have grave consequences (attempted suicide). Dysthyroidism may occur. Furthermore, treatment with interferon is counter-indicated during pregnancy.
Regarding ribavirin, the principal side effect is haemolytic anaemia. Anaemia may lead to treatment being stopped in approximately 5% of cases. Decompensation due to an underlying cardiopathy or coronaropathy linked to anaemia may arise.
Neutropenia is observed in approximately 20% of patients receiving a combination of pegylated interferon and ribavirin, and represents the major grounds for reducing the pegylated interferon dose.
The cost of these treatments is also very high.
In order to be able to predict, before having even commenced administration of the anti-HCV treatment, whether a given patient will or will not respond to treatment is thus of major clinical and economic importance.
Research into predictive means of this type has led to various clinical, biological and viral factors being analysed.
Certain clinical factors of the patient, such as age, weight, ethnic origin and hepatic fibrosis score are known to influence the efficacy of anti-HCV treatment.
As an example, the number of patients responding to anti-HCV treatment is lower among patients with a hepatic fibrosis score of F3 or F4 compared with those for whom the hepatic fibrosis score is F1 or F2 (scores using the Metavir F score system).
Of themselves, however, these clinical factors cannot be used to reliably predict, prior to starting a treatment, whether a given patient will or will not respond to an anti-HCV treatment.
Thus, of themselves, these factors are not good pre-therapeutic prognostic indicators.
In order to attempt to predict, before administering any treatment, whether a patient will or will not respond to an anti-HCV treatment, in fact it is viral factors which are currently being used.
It has in fact been shown that patients who are infected with an HCV of genotype 2 or 3 respond better to anti-HCV treatment than those who are infected with HCV of genotype 5 or 6, who in turn respond better to anti-HCV treatment than those who are infected with an HCV of genotype 1 or 4.
However, the distribution of the various genotypes is not homogeneous with respect to geographical locations, and thus simply discerning the viral genotype does not provide a predictive solution which can be applied to all patients.
What is more, there are differences between the viral sub-types.
In fact, knowledge of the nature of the viral genotype can essentially be used to adjust the posology and/or duration of treatment, but cannot per se be used to establish a reliable prediction before starting treatment.
Various combinations of biological and/or clinical and/or viral factors have also been tested in order to attempt to predict, before administering any treatment, whether a patient will or will not respond to an anti-HCV treatment. However, the combinations which have been tested up to now have not achieved satisfactorily predictive performances.
As an example, Hidetsugu Saito et al. 2010 succeeded in identifying combinations of biological, clinical and viral factors which gave reliable predictive performances when they were applied during treatment, but they were not at all able to identify a combination which was sufficiently reliable when applied before starting anti-HCV treatment.
Chen et al. 2005 and Chen et al. 2010 proposed a transcriptome signature for predicting, before any anti-HCV treatment was administered, whether a patient would be a responder or non-responder to this treatment. That signature combined the levels of expression of eighteen genes (G1P2, OAS2, G1P3, OAS3, RPLP2, CEB1, IFIT1, VIPERIN, RPS28, PI3KAP1, MX1, DUSP1, ATF5, LAP3, USP18, LGP1, ETEF1 and STXBP5).
Further, at least two of those genes code for proteins which are exclusively membrane proteins (G1P3 and VIPERIN); thus, the product of the expression thereof cannot be detected in the bloodstream.
Asselah et al. 2008 analysed the level of expression of fifty-eight genes before applying anti-HCV treatment to forty patients with chronic hepatitis C, fourteen of whom were non-responders to anti-HCV treatment. They thus identified two signatures which might be able to predict, before administering any anti-HCV treatment, whether a patient would be a non-responder to that treatment.
The first signature was based on the levels of expression of two genes, namely IF127 and CXCL9, which were analysed using the KNN method (k-nearest neighbour method).
The second signature was based on the levels of expression of three genes, namely IF127, CXCL9 and IFI-6-16, which were analysed using the WV method (weighted voting method).
For each of these two signatures, Asselah et al. 2008 indicated that the fact of adding supplemental genes did not allow the accuracy of the classification to be improved.
Thus, there is still a need for means which could be used to predict, even before commencing to administer the anti-HCV treatment, whether the patient has a high probability of responding or, in contrast, a high probability of not responding to treatment.